Introduction to special section: Mining and minerals exploration interpretation
Notice bibliographique
Résumé
Mineral deposits are found in a variety of geologic settings and ore-forming minerals can have a vast range of physical properties. The search for these deposits is also relatively near-surface thus far. These factors allow for a large number of possible airborne and ground-based techniques to be used in geophysical exploration. Deciding on the proper geophysical technique and survey layout requires an understanding of the target, its associated alteration, the variations in physical properties and the geologic and structural setting. Knowing the exploration history is important, particularly in exploration programs that are more mature. Interpretation of the data requires the integration of the myriad of information ranging from physical property models constructed from inversions or forward modeling, physical property data, geochemical data, mineral deposit model, and host geology. \nWe envisioned a special section on mining geophysics to highlight the integrative nature of mining geophysics through a collection of papers using multiple geophysical data to provide geology and exploration rationale along with the interpretations. So in collaboration with the editor of Interpretation, we issued a call for papers that discussed geophysics as applied to mining, discussed all relevant geophysical data and provided geologic information and the exploration rationale along with the interpretations. The following papers provide insight into the importance of geophysics in mineral exploration from the belt or camp scale to exploration focused on a specific orebody. \nWright and Koehler combine controlled-source audio magnetotelluric and gravity data in a previously explored terrain of the Carlin trend. The authors demonstrate that successive geophysical surveys, combined with geologic understanding and target model development were key to a significant gold discovery. \nMartinez and Li demonstrate that lithological interpretation techniques based on inversion of airborne gravity gradiometry and aeromagnetic data can be used to characterize an iron-ore formation in Minas Gerais. The authors show that lithology differentiation using either generic physical property constraints or geologic constraints can contribute to a geologic understanding at the deposit scale. \nOlaniyan et al. study the 3D geologic and structural setting of the Sudbury structure using an integration of geologic data with airborne gravity and magnetic data. Using standard 2.5D modeling and 3D Geomodeller software, they generate continuous surfaces in three dimensions for each geologic interface, which leads them to suggest a possible deformation history of the Sudbury structure. \nWoolrych et al. present data from a range of airborne and ground-based geophysical techniques that have contributed to the discovery of the Kitumba iron oxide copper gold (IOCG) deposit in central Zambia. The interpretation of geophysical data following an exploration criteria of an IOCG-type deposit model has opened up exploration for this style of deposit in Central Zambia. \nLü et al. present a case study that demonstrates the use of integrating seismic, magnetotelluric, gravity, and magnetic data to interpret the 3D structure and deformation at depth in the Lu-Zong ore district of Eastern China. Insights were obtained into the fault systems and crustal architecture that are essential for understanding the Lu-Zong ore district mineral system and for mineral exploration at depth. \nLegault et al. present the results of three different airborne electromagnetic (EM) surveys over the Lalor copper-zinc-gold volcanogenic massive sulfide deposit, which is more than 500 m deep and is in the Flin Flon Greenstone Belt of north-central Manitoba. The active and passive source EM surveys span a five year period, which means that the development of EM systems over this period can be assessed.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».